{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 创建画布对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 指定子图位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "# 指定子图的具体位置分布\n",
    "charts1 = fig.add_subplot(1,2,1) # 1行2列第1个图\n",
    "charts2 = fig.add_subplot(1,2,2) # 1行2列第2个图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 在每个空白子图中插入图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "charts1 = fig.add_subplot(1,2,1)\n",
    "# 第一个子图的图形代码\n",
    "plt.scatter(\n",
    "    [1,2,3],\n",
    "    [6,4,2]\n",
    ")\n",
    "plt.title('散点图')\n",
    "charts2 = fig.add_subplot(1,2,2)\n",
    "# 第二个子图的图形代码\n",
    "plt.bar(\n",
    "    ['A','B','C'],\n",
    "    [1,2,3]\n",
    ")\n",
    "plt.title('柱形图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
